Frequency-selectivity of a thalamocortical relay neuron during Parkinson’s disease and deep brain stimulation: a computational study

The Expanding Horizon of Neural Stimulation for Hyperkinetic Movement Disorders

Novel methods of neural stimulation are transforming the management of hyperkinetic movement disorders. In this review the diversity of approach available is showcased. We first describe the most commonly used features that can be extracted from oscillatory activity of the central nervous system, and how these can be combined with an expanding range of non-invasive and invasive brain stimulation techniques. We then shift our focus to the periphery using tremor and Tourette's syndrome to illustrate the utility of peripheral biomarkers and interventions. Finally, we discuss current innovations which are changing the landscape of stimulation strategy by integrating technological advances and the use of machine learning to drive optimization.

Deep brain stimulation: current challenges and future directions

The clinical use of deep brain stimulation (DBS) is among the most important advances in the clinical neurosciences in the past two decades. As a surgical tool, DBS can directly measure pathological brain activity and can deliver adjustable stimulation for therapeutic effect in neurological and psychiatric disorders correlated with dysfunctional circuitry. The development of DBS has opened new opportunities to access and interrogate malfunctioning brain circuits and to test the therapeutic potential of regulating the output of these circuits in a broad range of disorders. Despite the success and rapid adoption of DBS, crucial questions remain, including which brain areas should be targeted and in which patients. This Review considers how DBS has facilitated advances in our understanding of how circuit malfunction can lead to brain disorders and outlines the key unmet challenges and future directions in the DBS field. Determining the next steps in DBS science will help to define the future role of this technology in the development of novel therapeutics for the most challenging disorders affecting the human brain.

Optimized temporal pattern of brain stimulation designed by computational evolution

Brain stimulation is a promising therapy for several neurological disorders, including Parkinson's disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson's disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson's disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.

Stimulating at the right time: phase-specific deep brain stimulation

SEE MOLL AND ENGEL DOI101093/AWW308 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Brain regions dynamically engage and disengage with one another to execute everyday actions from movement to decision making. Pathologies such as Parkinson's disease and tremor emerge when brain regions controlling movement cannot readily decouple, compromising motor function. Here, we propose a novel stimulation strategy that selectively regulates neural synchrony through phase-specific stimulation. We demonstrate for the first time the therapeutic potential of such a stimulation strategy for the treatment of patients with pathological tremor. Symptom suppression is achieved by delivering stimulation to the ventrolateral thalamus, timed according to the patient's tremor rhythm. Sustained locking of deep brain stimulation to a particular phase of tremor afforded clinically significant tremor relief (up to 87% tremor suppression) in selected patients with essential tremor despite delivering less than half the energy of conventional high frequency stimulation. Phase-specific stimulation efficacy depended on the resonant characteristics of the underlying tremor network. Selective regulation of neural synchrony through phase-locked stimulation has the potential to both increase the efficiency of therapy and to minimize stimulation-induced side effects.

Exploiting pallidal plasticity for stimulation in Parkinson's disease

OBJECTIVE

Continuous application of high-frequency deep brain stimulation (DBS) often effectively reduces motor symptoms of Parkinson's disease patients. While there is a growing need for more effective and less traumatic stimulation, the exact mechanism of DBS is still unknown. Here, we present a methodology to exploit the plasticity of GABAergic synapses inside the external globus pallidus (GPe) for the optimization of DBS.

APPROACH

Assuming the existence of spike-timing-dependent plasticity (STDP) at GABAergic GPe-GPe synapses, we simulate neural activity in a network model of the subthalamic nucleus and GPe. In particular, we test different DBS protocols in our model and quantify their influence on neural synchrony.

MAIN RESULTS

In an exemplary set of biologically plausible model parameters, we show that STDP in the GPe has a direct influence on neural activity and especially the stability of firing patterns. STDP stabilizes both uncorrelated firing in the healthy state and correlated firing in the parkinsonian state. Alternative stimulation protocols such as coordinated reset stimulation can clearly profit from the stabilizing effect of STDP. These results are widely independent of the STDP learning rule.

SIGNIFICANCE

Once the model settings, e.g., connection architectures, have been described experimentally, our model can be adjusted and directly applied in the development of novel stimulation protocols. More efficient stimulation leads to both minimization of side effects and savings in battery power.

Tic related local field potentials in the thalamus and the effect of deep brain stimulation in Tourette syndrome: Report of three cases

OBJECTIVE

Three patients with intractable Tourette syndrome (TS) underwent thalamic deep brain stimulation (DBS). To investigate the role of thalamic electrical activity in tic generation, local field potentials (LFP), EEG and EMG simultaneously were recorded.

METHODS

Event related potentials and event related spectral perturbations of EEG and LFP, event related cross-coherences between EEG/LFP and LFP/LFP were analyzed. As time locking events, the tic onsets were used. Spontaneous tics were compared to voluntary tic mimicking. The effect of tic suppression and DBS on thalamic LFPs was evaluated.

RESULTS

All three patients showed time-locked and prior to onset of spontaneous motor tics thalamic synchronization and thalamo-cortical cross-coherence. Also in three patients, not time-locked to motor tics, increased intra-thalamic coherences in the 1-8Hz frequency band were found. In one patient it was demonstrated that voluntary mimicked tics were preceded by premotor cortical and thalamic potentials. In this patient unilateral thalamic DBS contralaterally decreased the background thalamic activity.

CONCLUSIONS

The present study in three cases with TS shows that spontaneous tics in TS are preceded by repetitive coherent thalamo-cortical discharges, indicating that preceding a tic the basal ganglia circuits are "charged up", ultimately leading to a motor tic.

SIGNIFICANCE

Thalamic LFP recording may lead to more insight in underlying pathophysiological mechanisms in TS.

The highs and lows of beta activity in cortico-basal ganglia loops

Oscillatory activity in the beta (13-30 Hz) frequency band is widespread in cortico-basal ganglia circuits, and becomes prominent in Parkinson's disease (PD). Here we develop the hypothesis that the degree of synchronization in this frequency band is a critical factor in gating computation across a population of neurons, with increases in beta band synchrony entailing a loss of information-coding space and hence computational capacity. Task and context drive this dynamic gating, so that for each state there will be an optimal level of network synchrony, and levels lower or higher than this will impair behavioural performance. Thus, both the pathological exaggeration of synchrony, as observed in PD, and the ability of interventions like deep brain stimulation (DBS) to excessively suppress synchrony can potentially lead to impairments in behavioural performance. Indeed, under physiological conditions, the manipulation of computational capacity by beta activity may itself present a mechanism of action selection and maintenance.

Neural circuit modulation during deep brain stimulation at the subthalamic nucleus for Parkinson's disease: what have we learned from neuroimaging studies?

Deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) represents a powerful clinical tool for the alleviation of many motor symptoms that are associated with Parkinson's disease. Despite its extensive use, the underlying therapeutic mechanisms of STN-DBS remain poorly understood. In the present review, we integrate and discuss recent literature examining the network effects of STN-DBS for Parkinson's disease, placing emphasis on neuroimaging findings, including functional magnetic resonance imaging, positron emission tomography, and single-photon emission computed tomography. These techniques enable the noninvasive detection of brain regions that are modulated by DBS on a whole-brain scale, representing a key experimental strength given the diffuse and far-reaching effects of electrical field stimulation. By examining these data in the context of multiple hypotheses of DBS action, generally developed through clinical and physiological observations, we define a multitude of consistencies and inconsistencies in the developing literature of this rapidly moving field.

Phase dependent modulation of tremor amplitude in essential tremor through thalamic stimulation

High frequency deep brain stimulation of the thalamus can help ameliorate severe essential tremor. Here we explore how the efficacy, efficiency and selectivity of thalamic deep brain stimulation might be improved in this condition. We started from the hypothesis that the effects of electrical stimulation on essential tremor may be phase dependent, and that, in particular, there are tremor phases at which stimuli preferentially lead to a reduction in the amplitude of tremor. The latter could be exploited to improve deep brain stimulation, particularly if tremor suppression could be reinforced by cumulative effects. Accordingly, we stimulated 10 patients with essential tremor and thalamic electrodes, while recording tremor amplitude and phase. Stimulation near the postural tremor frequency entrained tremor. Tremor amplitude was also modulated depending on the phase at which stimulation pulses were delivered in the tremor cycle. Stimuli in one half of the tremor cycle reduced median tremor amplitude by ∼10%, while those in the opposite half of the tremor cycle increased tremor amplitude by a similar amount. At optimal phase alignment tremor suppression reached 27%. Moreover, tremor amplitude showed a non-linear increase in the degree of suppression with successive stimuli; tremor suppression was increased threefold if a stimulus was preceded by four stimuli with a similar phase relationship with respect to the tremor, suggesting cumulative, possibly plastic, effects. The present results pave the way for a stimulation system that tracks tremor phase to control when deep brain stimulation pulses are delivered to treat essential tremor. This would allow treatment effects to be maximized by focussing stimulation on the optimal phase for suppression and by ensuring that this is repeated over many cycles so as to harness cumulative effects. Such a system might potentially achieve tremor control with far less power demand and greater specificity than current high frequency stimulation approaches, and may lower the risk for tolerance and rebound.

Using a hybrid neuron in physiologically inspired models of the basal ganglia

Our current understanding of the basal ganglia (BG) has facilitated the creation of computational models that have contributed novel theories, explored new functional anatomy and demonstrated results complementing physiological experiments. However, the utility of these models extends beyond these applications. Particularly in neuromorphic engineering, where the basal ganglia's role in computation is important for applications such as power efficient autonomous agents and model-based control strategies. The neurons used in existing computational models of the BG, however, are not amenable for many low-power hardware implementations. Motivated by a need for more hardware accessible networks, we replicate four published models of the BG, spanning single neuron and small networks, replacing the more computationally expensive neuron models with an Izhikevich hybrid neuron. This begins with a network modeling action-selection, where the basal activity levels and the ability to appropriately select the most salient input is reproduced. A Parkinson's disease model is then explored under normal conditions, Parkinsonian conditions and during subthalamic nucleus deep brain stimulation (DBS). The resulting network is capable of replicating the loss of thalamic relay capabilities in the Parkinsonian state and its return under DBS. This is also demonstrated using a network capable of action-selection. Finally, a study of correlation transfer under different patterns of Parkinsonian activity is presented. These networks successfully captured the significant results of the originals studies. This not only creates a foundation for neuromorphic hardware implementations but may also support the development of large-scale biophysical models. The former potentially providing a way of improving the efficacy of DBS and the latter allowing for the efficient simulation of larger more comprehensive networks.

Basal ganglia activity patterns in parkinsonism and computational modeling of their downstream effects

The availability of suitable animal models and the opportunity to record electrophysiologic data in movement disorder patients undergoing neurosurgical procedures has allowed researchers to investigate parkinsonism-related changes in neuronal firing patterns in the basal ganglia and associated areas of the thalamus and cortex. These studies have shown that parkinsonism is associated with increased activity in the basal ganglia output nuclei, along with increases in burst discharges, oscillatory firing and synchronous firing patterns throughout the basal ganglia. Computational approaches have the potential to play an important role in the interpretation of these data. Such efforts can provide a formalized view of neuronal interactions in the network of connections between the basal ganglia, thalamus, and cortex, allow for the exploration of possible contributions of particular network components to parkinsonism, and potentially result in new conceptual frameworks and hypotheses that can be subjected to biological testing. It has proven very difficult, however, to integrate the wealth of the experimental findings into coherent models of the disease. In this review, we provide an overview of the abnormalities in neuronal activity that have been associated with parkinsonism. Subsequently, we discuss some particular efforts to model the pathophysiologic mechanisms that may link abnormal basal ganglia activity to the cardinal parkinsonian motor signs and may help to explain the mechanisms underlying the therapeutic efficacy of deep brain stimulation for Parkinson's disease. We emphasize the logical structure of these computational studies, making clear the assumptions from which they proceed and the consequences and predictions that follow from these assumptions.

Performance limitations of relay neurons

Relay cells are prevalent throughout sensory systems and receive two types of inputs: driving and modulating. The driving input contains receptive field properties that must be transmitted while the modulating input alters the specifics of transmission. For example, the visual thalamus contains relay neurons that receive driving inputs from the retina that encode a visual image, and modulating inputs from reticular activating system and layer 6 of visual cortex that control what aspects of the image will be relayed back to visual cortex for perception. What gets relayed depends on several factors such as attentional demands and a subject's goals. In this paper, we analyze a biophysical based model of a relay cell and use systems theoretic tools to construct analytic bounds on how well the cell transmits a driving input as a function of the neuron's electrophysiological properties, the modulating input, and the driving signal parameters. We assume that the modulating input belongs to a class of sinusoidal signals and that the driving input is an irregular train of pulses with inter-pulse intervals obeying an exponential distribution. Our analysis applies to any [Formula: see text] order model as long as the neuron does not spike without a driving input pulse and exhibits a refractory period. Our bounds on relay reliability contain performance obtained through simulation of a second and third order model, and suggest, for instance, that if the frequency of the modulating input increases or the DC offset decreases, then relay increases. Our analysis also shows, for the first time, how the biophysical properties of the neuron (e.g. ion channel dynamics) define the oscillatory patterns needed in the modulating input for appropriately timed relay of sensory information. In our discussion, we describe how our bounds predict experimentally observed neural activity in the basal ganglia in (i) health, (ii) in Parkinson's disease (PD), and (iii) in PD during therapeutic deep brain stimulation. Our bounds also predict different rhythms that emerge in the lateral geniculate nucleus in the thalamus during different attentional states.

Does suppression of oscillatory synchronisation mediate some of the therapeutic effects of DBS in patients with Parkinson's disease?

There is growing evidence for exaggerated oscillatory neuronal synchronisation in patients with Parkinson's disease (PD). In particular, oscillations at around 20 Hz, in the so-called beta frequency band, relate to the cardinal symptoms of bradykinesia and rigidity. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) can significantly improve these motor impairments. Recent evidence has demonstrated reduction of beta oscillations concurrent with alleviation of PD motor symptoms, raising the possibility that suppression of aberrant activity may mediate the effects of DBS. Here we review the evidence supporting suppression of pathological oscillations during stimulation and discuss how this might underlie the efficacy of DBS. We also consider how beta activity may provide a feedback signal suitable for next generation closed-loop and intelligent stimulators.

An analytical study of relay neuron's reliability: dependence on input and model parameters

Relay neurons are widely found in our nervous system, including the Thalamus, spinal cord and lateral geniculate body. They receive a modulating input (background activity) and a reference input. The modulating input modulates relay of the reference input. This modulation is critical for correct functioning of relay neurons, but is poorly understood. In this paper, we use a biophysical-based model and systems theoretic tools to calculate how well a single relay neuron relays a reference input signal as a function of the neuron's electro physiological properties (i.e. model parameters), the modulating signal, and the reference signal parameters. Our analysis is more rigorous than previous related works and is generalizable to all relay cells in the body. Our analytical expression matches relay performance obtained in simulation and suggest that increasing the frequency of a sinusoidal modulating input or decreasing its DC offset increases the relay cell reliability.

The effects of DBS patterns on basal ganglia activity and thalamic relay : a computational study

Thalamic neurons receive inputs from cortex and their responses are modulated by the basal ganglia (BG). This modulation is necessary to properly relay cortical inputs back to cortex and downstream to the brain stem when movements are planned. In Parkinson's disease (PD), the BG input to thalamus becomes pathological and relay of motor-related cortical inputs is compromised, thereby impairing movements. However, high frequency (HF) deep brain stimulation (DBS) may be used to restore relay reliability, thereby restoring movements in PD patients. Although therapeutic, HF stimulation consumes significant power forcing surgical battery replacements, and may cause adverse side effects. Here, we used a biophysical-based model of the BG-Thalamus motor loop in both healthy and PD conditions to assess whether low frequency stimulation can suppress pathological activity in PD and enable the thalamus to reliably relay movement-related cortical inputs. We administered periodic pulse train DBS waveforms to the sub-thalamic nucleus (STN) with frequencies ranging from 0-140 Hz, and computed statistics that quantified pathological bursting, oscillations, and synchronization in the BG as well as thalamic relay of cortical inputs. We found that none of the frequencies suppressed all pathological activity in BG, though the HF waveforms recovered thalamic reliability. Our rigorous study, however, led us to a novel DBS strategy involving low frequency multi-input phase-shifted DBS, which successfully suppressed pathological symptoms in all BG nuclei and enabled reliable thalamic relay. The neural restoration remained robust to changes in the model parameters characterizing early to late PD stages.

Correlation transfer from basal ganglia to thalamus in Parkinson's disease

Spike trains from neurons in the basal ganglia of parkinsonian primates show increased pairwise correlations, oscillatory activity, and burst rate compared to those from neurons recorded during normal brain activity. However, it is not known how these changes affect the behavior of downstream thalamic neurons. To understand how patterns of basal ganglia population activity may affect thalamic spike statistics, we study pairs of model thalamocortical (TC) relay neurons receiving correlated inhibitory input from the internal segment of the globus pallidus (GPi), a primary output nucleus of the basal ganglia. We observe that the strength of correlations of TC neuron spike trains increases with the GPi correlation level, and bursty firing patterns such as those seen in the parkinsonian GPi allow for stronger transfer of correlations than do firing patterns found under normal conditions. We also show that the T-current in the TC neurons does not significantly affect correlation transfer, despite its pronounced effects on spiking. Oscillatory firing patterns in GPi are shown to affect the timescale at which correlations are best transferred through the system. To explain this last result, we analytically compute the spike count correlation coefficient for oscillatory cases in a reduced point process model. Our analysis indicates that the dependence of the timescale of correlation transfer is robust to different levels of input spike and rate correlations and arises due to differences in instantaneous spike correlations, even when the long timescale rhythmic modulations of neurons are identical. Overall, these results show that parkinsonian firing patterns in GPi do affect the transfer of correlations to the thalamus.

Decoupling neuronal oscillations during subthalamic nucleus stimulation in the parkinsonian primate

Subthalamic nucleus (STN) stimulation is a popular treatment for Parkinson's disease; however, its effect on neuronal activity is unclear. We performed simultaneous multi-electrode recordings in the STN and its targets, the globus pallidus internus (GPi) and externus (GPe) in the parkinsonian non-human primate during high frequency STN macro-stimulation. Our results indicate that in the parkinsonian state the abnormal neuronal oscillatory activity in the 10-15 Hz range is coherent within and between nuclei. We further show that STN macro-stimulation results in a reduction of oscillatory activity in the globus pallidus. In addition, a functional decoupling of the STN from its pallidal targets is evidenced by the reduced STN-GPi coherence, that effectively removes the STN synchronous oscillatory drive of basal ganglia output. This decoupling results in reduced coherence between neurons within the GPi which resume an independent neuronal activity pattern. This decorrelation of the basal ganglia output may result in a reduction of the fluctuations of the basal ganglia inhibitory control over thalamic neurons which may potentially contribute to the beneficial effects of deep brain high-frequency stimulation.